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4,299,418 works, Canadian by any of four routes.

Every filter state is a URL; the URL is the query; the query is citable via /q/⟨hash⟩. The page, the API and the export parse the same parameters.

The current cohort, streamed from the database: every work column, the machine labels, the provisional scores, and the per-row validation status. Exports are capped at 100,000 rows. Mints a permanent /q/ link for this exact query. The same filters always produce the same link, whoever asks.

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Machine Learning in Healthcare
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Direct Codex and Gemma labels are unvalidated and sparse. Distilled predictions cover the full frame and are also unvalidated. Choose the evidence source explicitly; absence of a direct label is never a negative label.

affaffiliation
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The four routes compose: require the funder route and exclude affiliation to get the funder-only stratum no affiliation-based frame ever sees.

1,008 results · 1 filter active ·
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20002025
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Machine labels · sparse coverage
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An unlabeled work is unknown, not a negative. Label coverage is reported on every query.
1,008 works in the cohort · of 4,299,418page 11 of 21

Labels cover 4 of 1,008 works in this cohort. The rest are unlabeled, which is not a negative label: the label table is sparse today and grows as labeling rounds land.

Distilled predictions cover 1,008 of 1,008 works in this cohort. Predictions are machine_predicted_unvalidated teacher distillation outputs. Candidate is the union; consensus is the intersection.

fundno affunlabeled
Ensemble Modeling for the Classification of Birth Data
Fiaz Majeed, Abdul Razzaq Ahmad Shakir, Maqbool Ahmad, Shahzada Khurram, Muhammad Qaiser Saleem, Muhammad Shafiq +3 more
2024· article· en· Intelligent Automation & Soft Computing· Computer Science
distilled prediction:candidate · noneconsensus · none
3
citations
affno abstractunlabeled
Maximally Machine-Learnable Portfolios
Philippe Goulet Coulombe, Maximilian Göbel
2023· article· en· SSRN Electronic Journal· Computer Science
distilled prediction:candidate · research_integrity+insufficient_payloadconsensus · none
3
citations
fundno affno abstractunlabeled
Artificial Intelligence in Medicine
2024· book· en· Lecture notes in computer science· Computer Science
distilled prediction:candidate · metaepi_narrow+open_science+research_integrityconsensus · none
3
citations
affunlabeled
Deep learning for platelet transfusion
Na Li, Douglas G. Down
2023· editorial· en· Blood· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
3
citations
affunlabeled
Reflective Artificial Intelligence
Peter R. Lewis, Ştefan Sarkadi
2023· preprint· en· Research Portal (King's College London)· Computer Science
distilled prediction:candidate · metaepi_narrow+open_science+research_integrity+insufficient_payloadconsensus · open_science
3
citations
affunlabeled
The Challenges of Data Visualization for Precision Medicine
Tatiana Bevilacqua, Raphael Mendoza da Nobrega, Helen Chen, Plinio Pelegrini Morita
2019· article· en· Proceedings of the International Symposium on Human Factors and Ergonomics in Health Care· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
aboutno affunlabeled
Predictive Models in Personalized Medicine
Faisal Farooq, Balaji Krishnapuram, Rómer Rosales, Shipeng Yu, Jude Shavlik, Raju Kucherlapati
2011· article· en· ACM SIGHIT Record· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations
affno abstractunlabeled
Big Data Challenges from a Human Factors Perspective
André Kushniruk, Elizabeth M. Borycki
2019· book-chapter· en· Lecture notes in bioengineering· Computer Science
distilled prediction:candidate · metaepi_narrowconsensus · none
2
citations
affunlabeled
Predicting the Length of Stay of Patients in Hospitals
Zhiwei Fu, Xinran Gu, Jia Fu, Mojtaba Moattari, Farhana Zulkernine
2021· article· en· 2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)· Computer Science
distilled prediction:candidate · noneconsensus · none
2
citations

How this was built: Screen · Findings · About